The CQT was initially proposed for music signal processing [1]}. Then it was also applied in different speech processing tasks, e.g., anti-spoofing [2]}, [3]}, speaker verification [4]} and acoustic scene classification [5]}. Recently, the CQT has also been studied for SER [6]}, but without success. This is possibly due to the lack of optimization of CQT parameters and/or the applied end-to-end model fails to exploit the advantages of CQT. Recent studies show that CNN-based models are suitable for SER including cross-corpora evaluation [7]}, [8]}. In this work, we also adopt a CNN-based approach for modeling SER systems. Our main contributions in this work are summarized as follows: (i) We propose a new framework for CQT-based SER by optimizing CQT extraction parameters for reduced redundancy and improved performance, (ii) We investigate CNN architecture known as time-delay neural networks (TDNNs) suitable for speech pattern classification tasks [9]}, [10]} for SER, and (iii) We perform cross-corpora evaluation with three different speech corpora to assess the generalization ability of the proposed method. Our results demonstrate that the optimized CQT features not only outperform short-time Fourier transform (STFT) features but also provide better generalization.
